Switching diffusion approximations for optimal power management in parallel processing systems

2021 ◽  
pp. 1-43
Author(s):  
Saul C. Leite ◽  
Marcelo D. Fragoso ◽  
Rodolfo S. Teixeira
Author(s):  
Richard T. Meyer ◽  
Raymond A. DeCarlo ◽  
Steve D. Pekarek ◽  
Jing Sun ◽  
Hyeongjun Park

Power management of a ship’s electrical system has become important due to increasing loads from manpower-reducing automation, greater power requirements of advanced weapons and sensors, introduction of all electric propulsion, and the increasing cost of oil-based fossil fuels. A coordinated power management strategy of the ship’s electric power grid is desired to optimally allocate power flows and minimize fuel consumption. This paper develops such an optimal power management system for an interconnected, supervisory-level ship power system model based upon a ship power system test bed developed for the Office of Naval Research. The ship power system consists of two electrical generators, one rated at 59 kW to represent a gas turbine engine-generator pair and the other rated at 11 kW to represent a diesel generator, an 8 kW pulsed power load that represents the discharge and charge of a capacitor bank for an electromagnetic railgun system, and 37 kW ship propulsion system comprised of an induction motor coupled to the propeller shaft. The ship propulsion system’s induction motor has switched operation with two modes of operation, propelling and generating; the latter mode means that excess kinetic energy during ship slowing can be used to charge the capacitor bank for loads such as pulsed power loads. Given the switched system model, the paper sets forth a hybrid model predictive control strategy based on a minimization of a performance index that trades off fuel consumption, velocity tracking error, and electrical bus voltage error. The optimization is performed using a relaxed representation of the control problem (termed the embedding method) and collocation for discretization with traditional numerical programming to compute the mode and continuous control inputs. The methodology avoids the computational complexity associated with alternative approaches, e.g., mixed-integer programming. Numerical optimization is performed with MATLAB’s sqpLineSearch. To demonstrate the power management approach, a scenario is simulated where the ship is to follow a changing desired velocity while simultaneously maintaining the bus voltage at a desired value, keeping the 11 kW generator at a fuel efficient operating point, and minimizing the fuel use of the 59 kW generator.


2018 ◽  
Vol 43 (42) ◽  
pp. 19336-19351 ◽  
Author(s):  
Ilse Cervantes ◽  
Marco Hernandez-Nochebuena ◽  
Ulises Cano-Castillo ◽  
Ismael Araujo-Vargas

Author(s):  
Hui Liu ◽  
Xunming Li ◽  
Weida Wang ◽  
Lijin Han ◽  
Huibin Xin ◽  
...  

An adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy for a four-wheel drive plug-in hybrid electric vehicle is proposed in this paper. The equivalent factors of adaptive equivalent consumption minimisation strategy are optimised offline based on ISIGHT software over several typical driving cycles, which is integrated with AVL CRUISE and MATLAB/Simulink. To update the equivalent factor adaptively according to the predictive velocity, a neural network-based optimal equivalent factor prediction model is built, which can be used online. The torque distribution strategy considering axle load based on energy management strategy optimisation results and the vehicle dynamics control distribution is proposed: this includes two-wheel drive torque distribution, four-wheel drive torque distribution and brake torque distribution. The proposed energy management strategy is verified in New European Driving Cycle and Worldwide harmonised Light Vehicle Test Cycle driving patterns, and the simulation results show that the fuel economy of adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy is improved by 8.84% and 7.52% in New European Driving Cycle and Worldwide harmonised Light Vehicle Test Cycle, respectively, compared with the benchmark algorithm-based strategy.


Author(s):  
Thanh Long Vu ◽  
Jaspreet Singh Dhupia ◽  
Aaron Alexander Ayu ◽  
Louis Kennedy ◽  
Alf Kare Adnanes

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